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README.md

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+# LearningToOptimize
+
+## 1. Introduction
+
+**LearningToOptimize** is an organization dedicated to **learning to optimize (L2O)** — an emerging paradigm where machine learning models *learn* to solve optimization problems efficiently. This approach is also known as using **optimization proxies** or **amortized optimization**. Our mission is to serve as a hub for sharing open datasets, pre-trained models, and tools that accelerate research and practical applications of L2O methods.
+
+This organization is closely linked to the [LearningToOptimize.jl](https://github.com/andrewrosemberg/LearningToOptimize.jl) Julia package, which provides foundational functionalities for fitting ML-based surrogate models (or proxies) to complex optimization problems. Here, you will find:
+
+- **Datasets**: Collections of problem instances and their optimal solutions, useful for training and benchmarking.  
+- **Trained Models**: Ready-to-use optimization proxies for various tasks, enabling rapid inference on new problem instances.  
+- **Benchmarking Tools**: Utilities for comparing learned proxies against traditional solvers in terms of speed, feasibility, and performance.
+
+## 2. What are Optimization Proxies?
+
+### High-Level Explanation
+
+*Optimization proxies* are machine learning models that approximate or replace traditional optimization solvers. By observing many instances of a problem (and possibly their solutions), a proxy learns to predict near-optimal solutions in a single forward pass. This amortized approach can reduce or eliminate the need to run a time-consuming solver from scratch for each new instance, delivering **major speed-ups** in real-world applications such as power systems, resource allocation, and beyond.
+
+### Technical Explanation
+
+In more technical terms, **amortized optimization** seeks to learn a function \\( f_\\theta(x) \\) that maps problem parameters \\( x \\) to solutions \\( y \\) that (approximately) minimize a given objective function subject to constraints. Modern methods leverage techniques like **differentiable optimization layers**, **input-convex neural networks**, or constraint-enforcing architectures (e.g., [DC3](https://openreview.net/pdf?id=0Ow8_1kM5Z)) to ensure that the learned proxy solutions are both feasible and performant. By coupling the solver and the model in an **end-to-end** pipeline, these approaches let the training objective directly reflect downstream metrics, improving speed and reliability.
+
+Recent advances also focus on **trustworthy** or **certifiable** proxies, where constraint satisfaction or performance bounds are guaranteed. This is crucial in domains like energy systems or manufacturing, where infeasible solutions can have large penalties or safety concerns. Overall, learning-based optimization frameworks aim to combine the advantages of ML (data-driven generalization) with the rigor of mathematical programming (constraint handling and optimality).
+
+For a broader overview, see the [SIAM News article on trustworthy optimization proxies](https://www.siam.org/publications/siam-news/articles/fusing-artificial-intelligence-and-optimization-with-trustworthy-optimization-proxies/), which highlights the growing synergy between AI and classical optimization.
+
+## 3. References and Citations
+
+1. **A. Rosemberg, M. Tanneau, B. Fanzeres, J. Garcia, P. Van Hentenryck (2023)**  
+   *Learning Optimal Power Flow Value Functions with Input-Convex Neural Networks.*  
+   Accepted at *PSCC 2024*.
+
+3. **P. Donti, B. Amos, J. Z. Kolter (2021)**  
+   *DC3: A Learning Method for Optimization with Hard Constraints.*  
+   [ICLR](https://openreview.net/forum?id=0Ow8_1kM5Z)
+
+4. **P. Van Hentenryck (2023)**  
+   *Fusing Artificial Intelligence and Optimization with Trustworthy Optimization Proxies.*  
+   [SIAM News](https://www.siam.org/publications/siam-news/articles/fusing-artificial-intelligence-and-optimization-with-trustworthy-optimization-proxies/)
+
+5. **B. Amos (2022)**  
+   *Tutorial on Amortized Optimization.*  
+   [arXiv:2202.00665](https://arxiv.org/abs/2202.00665)
+
+2. **A. Rosemberg, A. Street, D. M. Valladão, P. Van Hentenryck (2023)**  
+   *Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality.*  
+   [arXiv:2405.14973](https://arxiv.org/abs/2405.14973)
+
+---
+
+By sharing our work and resources here, we hope to foster collaboration among researchers and practitioners who are exploring the exciting intersections of AI and optimization. Thank you for visiting **LearningToOptimize**—let’s push the boundaries of what’s possible in end-to-end optimization together!
+
+Please reach out if you want to contribute!